This script further analyzed the wt cells that are not from the Shh lineage.

library(dplyr)
library(Matrix)
library(Seurat)
load("seuratE15E16P1P4_wt.RData")
seurat_E15E16P1P4_wt<-SetAllIdent(object = seurat_E15E16P1P4_wt, id = "res.1.4")
E15E16P1P4_wt_nonShh = SubsetData(object=seurat_E15E16P1P4_wt,ident.use=c(26,10,12,6,0,17,18,21,14,31,23,33,22,25,5,4,13,15,19,16),min.cells = 1,min.genes = 1,project="E15Oct10_E16Dec7_P1Dec11_P4Oct18_wt_nonShh")
change metadata annotation to reflect the original (old, from seurat_E15E16P1P4_wt) or new (non-Shh) results:
colnames(E15E16P1P4_wt_nonShh@meta.data)[colnames(E15E16P1P4_wt_nonShh@meta.data) == 'res.0.8'] <- 'orig.0.8'
colnames(E15E16P1P4_wt_nonShh@meta.data)[colnames(E15E16P1P4_wt_nonShh@meta.data) == 'res.1.4'] <- 'orig.1.4'
colnames(E15E16P1P4_wt_nonShh@meta.data)[colnames(E15E16P1P4_wt_nonShh@meta.data) == 'res.1.6'] <- 'orig.1.6'
colnames(E15E16P1P4_wt_nonShh@meta.data)[colnames(E15E16P1P4_wt_nonShh@meta.data) == 'CellCycle_score'] <- 'orig.CellCycle_score'
colnames(E15E16P1P4_wt_nonShh@meta.data)[colnames(E15E16P1P4_wt_nonShh@meta.data) == 'mucosaGoblet_score'] <- 'orig.mucosaGoblet_score'
colnames(E15E16P1P4_wt_nonShh@meta.data)[colnames(E15E16P1P4_wt_nonShh@meta.data) == 'ciliopathy_score'] <- 'orig.ciliopathy_score'
colnames(E15E16P1P4_wt_nonShh@meta.data)[colnames(E15E16P1P4_wt_nonShh@meta.data) == 'PCD_score'] <- 'orig.PCD_score'
colnames(E15E16P1P4_wt_nonShh@meta.data)[colnames(E15E16P1P4_wt_nonShh@meta.data) == 'Bronchiectasis_Bronchitis'] <- 'orig.Bronchiectasis_Bronchitis_score'
E15E16P1P4_wt_nonShh <- ScaleData(object = E15E16P1P4_wt_nonShh)
Scaling data matrix

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E15E16P1P4_wt_nonShh <- FindVariableGenes(object = E15E16P1P4_wt_nonShh, do.plot = TRUE, x.low.cutoff=0.1,x.high.cutoff = Inf, y.cutoff = 0.5)
Calculating gene means
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variance to mean ratios
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|

E15E16P1P4_wt_nonShh <- RunPCA(object = E15E16P1P4_wt_nonShh,pcs.compute = 30, do.print = FALSE)
E15E16P1P4_wt_nonShh <- ProjectPCA(object = E15E16P1P4_wt_nonShh, do.print = FALSE)
PCHeatmap(object = E15E16P1P4_wt_nonShh, pc.use = c(1:12), cells.use = 500, do.balanced = TRUE, label.columns = FALSE, num.genes = 30)

PCElbowPlot(object = E15E16P1P4_wt_nonShh,num.pc = 30)

n.pcs = 27
res.used <- 0.8
E15E16P1P4_wt_nonShh <- FindClusters(object = E15E16P1P4_wt_nonShh, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE)
E15E16P1P4_wt_nonShh <- RunTSNE(object = E15E16P1P4_wt_nonShh, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2)
TSNEPlot(object = E15E16P1P4_wt_nonShh, do.label = T,pt.size = 0.2,group.by="res.0.8")

TSNEPlot(object = E15E16P1P4_wt_nonShh, do.label = F,group.by="seq_group",pt.size = 0.2)

res.used <- 1.0
E15E16P1P4_wt_nonShh <- FindClusters(object = E15E16P1P4_wt_nonShh, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE)
Build parameters exactly match those of already computed and stored SNN. To force recalculation, set force.recalc to TRUE.
E15E16P1P4_wt_nonShh <- RunTSNE(object = E15E16P1P4_wt_nonShh, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2,k.param=10)
TSNEPlot(object = E15E16P1P4_wt_nonShh, do.label = T,pt.size = 0.2,group.by="res.1")

res.used <- 1.2
E15E16P1P4_wt_nonShh <- FindClusters(object = E15E16P1P4_wt_nonShh, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE)
Build parameters exactly match those of already computed and stored SNN. To force recalculation, set force.recalc to TRUE.
E15E16P1P4_wt_nonShh <- RunTSNE(object = E15E16P1P4_wt_nonShh, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2,k.param=10)
TSNEPlot(object = E15E16P1P4_wt_nonShh, do.label = T,pt.size = 0.2,group.by="res.1.2")

res.used <- 1.6
E15E16P1P4_wt_nonShh <- FindClusters(object = E15E16P1P4_wt_nonShh, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE)
Build parameters exactly match those of already computed and stored SNN. To force recalculation, set force.recalc to TRUE.
E15E16P1P4_wt_nonShh <- RunTSNE(object = E15E16P1P4_wt_nonShh, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2,k.param=10)
TSNEPlot(object = E15E16P1P4_wt_nonShh, do.label = T,pt.size = 0.2,group.by="res.1.6")

We will use resolution=1.6 for the rest of the analysis.
E15E16P1P4_wt_nonShh <- SetAllIdent(object = E15E16P1P4_wt_nonShh, id = "res.1.6")
E15E16P1P4_wt_nonShh_res16_c31<-FindMarkers(E15E16P1P4_wt_nonShh,ident.1=c(31),only.pos = TRUE)

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E15E16P1P4_wt_nonShh_res16_c31
So cluster 31 is likely to be RBC.
library(plyr)
E15E16P1P4_wt_nonShh@meta.data$cell_type_1.6<-mapvalues(E15E16P1P4_wt_nonShh@meta.data$res.1.6,from=c("0","1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21","22","23","24","25","26","27","28","29","30","31","32"),to=c("Fibroblast","CyclingFibroblast","Chondrocyte","Fibroblast","Fibroblast","Fibroblast","Fibroblast","Fibroblast","Fibroblast","Fibroblast","CyclingFibroblast","Immune_1","CyclingFibroblast","Muscle","Fibroblast","VascularEndothelial","Muscle","MesenchymalProgenitor","Fibroblast","Chondrocyte","Immune_2","Chondrocyte","CyclingFibroblast","LymphaticEndothelial","Muscle","MesenchymalProgenitor","MesenchymalProgenitor","SchwannCell","MesenchymalProgenitor","VSMC/pericyte","Muscle","RBC","Neuron/NEC"))
wt_nonShh_cellType1.6<-E15E16P1P4_wt_nonShh@meta.data$cell_type_1.6
names(wt_nonShh_cellType1.6)<-E15E16P1P4_wt_nonShh@cell.names
#load("seuratE15E16P1P4_wt_nonShh.RData")

For the purpose of visualization, we average within each cell type:
E15E16P1P4_wt_nonShh<-SetAllIdent(object = E15E16P1P4_wt_nonShh, id = "cell_type_1.6")
average_wt_nonShh_res1.6_Annotation<-AverageExpression(object = E15E16P1P4_wt_nonShh,return.seurat = T)
Finished averaging RNA for cluster Chondrocyte
Finished averaging RNA for cluster CyclingFibroblast
Finished averaging RNA for cluster Fibroblast
Finished averaging RNA for cluster Immune_1
Finished averaging RNA for cluster Immune_2
Finished averaging RNA for cluster LymphaticEndothelial
Finished averaging RNA for cluster MesenchymalProgenitor
Finished averaging RNA for cluster Muscle
Finished averaging RNA for cluster Neuron/NEC
Finished averaging RNA for cluster RBC
Finished averaging RNA for cluster SchwannCell
Finished averaging RNA for cluster VascularEndothelial
Finished averaging RNA for cluster VSMC/pericyte
Performing log-normalization
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Scaling data matrix

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table(E15E16P1P4_wt_nonShh@meta.data$age[!(E15E16P1P4_wt_nonShh@meta.data$res.1.6 %in% c(31))],E15E16P1P4_wt_nonShh@meta.data$cell_type_1.6[!(E15E16P1P4_wt_nonShh@meta.data$res.1.6 %in% c(31))])

     
      Chondrocyte CyclingFibroblast Fibroblast Immune_1 Immune_2 LymphaticEndothelial MesenchymalProgenitor Muscle Neuron/NEC SchwannCell VascularEndothelial
  E15         326               320        390       45       12                    6                    91    100         35          36                  46
  E16         617               810       2914      163       78                   48                   199    285         15          53                 182
  P1            8               328        914      112       42                   53                   113    183          0           5                  19
  P4            5                58        470       27       34                   27                   108    152          1           0                  21
     
      VSMC/pericyte
  E15            10
  E16            59
  P1             10
  P4              1
neuron/NEC, and if they are from E15 or P4, are they from Shh lineage (if from Shh lineage, they should be captured in the “green” gate):
scaled data:

unscaled data:

Schwann cell:

some lineage markers:

Chondrocytes across time:

library(ggalluvial)

Error in library(ggalluvial) : there is no package called ‘ggalluvial’

Don’t expect a lot of Col10a1 or Mmp13:

ggplot(data=E15E16P1P4_wt_nonShh@meta.data,aes(axis1=orig.1.4,axis2=res.1.6,axis3=cell_type_1.6))+geom_alluvium(aes(fill=res.1.6))+geom_stratum(width = 1/12, fill = "black", color = "grey") +geom_label(stat = "stratum", label.strata = TRUE)+scale_x_discrete(limits = c("orig.1.4", "res.1.6","cell_type_1.6"), expand = c(.05, .05))

---
title: "Trachea_WT_10x_nonShh"
output: html_notebook
---
## This script further analyzed the wt cells that are not from the Shh lineage.
```{r}
library(dplyr)
library(Matrix)
library(Seurat)
```
```{r}
load("seuratE15E16P1P4_wt.RData")
```

```{r}
seurat_E15E16P1P4_wt<-SetAllIdent(object = seurat_E15E16P1P4_wt, id = "res.1.4")
#take non-Shh clusters:
E15E16P1P4_wt_nonShh = SubsetData(object=seurat_E15E16P1P4_wt,ident.use=c(26,10,12,6,0,17,18,21,14,31,23,33,22,25,5,4,13,15,19,16),min.cells = 1,min.genes = 1,project="E15Oct10_E16Dec7_P1Dec11_P4Oct18_wt_nonShh")
```
##### change metadata annotation to reflect the original (old, from seurat_E15E16P1P4_wt) or new (non-Shh) results:
```{r}
colnames(E15E16P1P4_wt_nonShh@meta.data)[colnames(E15E16P1P4_wt_nonShh@meta.data) == 'res.0.8'] <- 'orig.0.8'
colnames(E15E16P1P4_wt_nonShh@meta.data)[colnames(E15E16P1P4_wt_nonShh@meta.data) == 'res.1.4'] <- 'orig.1.4'
colnames(E15E16P1P4_wt_nonShh@meta.data)[colnames(E15E16P1P4_wt_nonShh@meta.data) == 'res.1.6'] <- 'orig.1.6'
```

```{r}
colnames(E15E16P1P4_wt_nonShh@meta.data)[colnames(E15E16P1P4_wt_nonShh@meta.data) == 'CellCycle_score'] <- 'orig.CellCycle_score'
colnames(E15E16P1P4_wt_nonShh@meta.data)[colnames(E15E16P1P4_wt_nonShh@meta.data) == 'mucosaGoblet_score'] <- 'orig.mucosaGoblet_score'
colnames(E15E16P1P4_wt_nonShh@meta.data)[colnames(E15E16P1P4_wt_nonShh@meta.data) == 'ciliopathy_score'] <- 'orig.ciliopathy_score'
colnames(E15E16P1P4_wt_nonShh@meta.data)[colnames(E15E16P1P4_wt_nonShh@meta.data) == 'PCD_score'] <- 'orig.PCD_score'
colnames(E15E16P1P4_wt_nonShh@meta.data)[colnames(E15E16P1P4_wt_nonShh@meta.data) == 'Bronchiectasis_Bronchitis'] <- 'orig.Bronchiectasis_Bronchitis_score'

```

```{r}
E15E16P1P4_wt_nonShh <- ScaleData(object = E15E16P1P4_wt_nonShh)
```


```{r}
E15E16P1P4_wt_nonShh <- FindVariableGenes(object = E15E16P1P4_wt_nonShh, do.plot = TRUE, x.low.cutoff=0.1,x.high.cutoff = Inf, y.cutoff = 0.5)
```
```{r}
E15E16P1P4_wt_nonShh <- RunPCA(object = E15E16P1P4_wt_nonShh,pcs.compute = 30, do.print = FALSE)
E15E16P1P4_wt_nonShh <- ProjectPCA(object = E15E16P1P4_wt_nonShh, do.print = FALSE)
```
```{r,fig.height=20,fig.width=8}
PCHeatmap(object = E15E16P1P4_wt_nonShh, pc.use = c(1:12), cells.use = 500, do.balanced = TRUE, label.columns = FALSE, num.genes = 30)

```

```{r}
PCElbowPlot(object = E15E16P1P4_wt_nonShh,num.pc = 30)
```
```{r}
n.pcs = 27
res.used <- 0.8

E15E16P1P4_wt_nonShh <- FindClusters(object = E15E16P1P4_wt_nonShh, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE)
```
```{r}
E15E16P1P4_wt_nonShh <- RunTSNE(object = E15E16P1P4_wt_nonShh, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2,k.param=10)

```

```{r}
TSNEPlot(object = E15E16P1P4_wt_nonShh, do.label = T,pt.size = 0.2,group.by="res.0.8")

```
```{r}
TSNEPlot(object = E15E16P1P4_wt_nonShh, do.label = F,group.by="seq_group",pt.size = 0.2)

```
```{r}
TSNEPlot(object = E15E16P1P4_wt_nonShh, do.label = F,group.by="age",pt.size = 0.2)

```

```{r}
res.used <- 1.0

E15E16P1P4_wt_nonShh <- FindClusters(object = E15E16P1P4_wt_nonShh, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE)
```
```{r}
E15E16P1P4_wt_nonShh <- RunTSNE(object = E15E16P1P4_wt_nonShh, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2,k.param=10)

```

```{r}
TSNEPlot(object = E15E16P1P4_wt_nonShh, do.label = T,pt.size = 0.2,group.by="res.1")

```

```{r}
res.used <- 1.2

E15E16P1P4_wt_nonShh <- FindClusters(object = E15E16P1P4_wt_nonShh, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE)
```
```{r}
E15E16P1P4_wt_nonShh <- RunTSNE(object = E15E16P1P4_wt_nonShh, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2,k.param=10)

```

```{r}
TSNEPlot(object = E15E16P1P4_wt_nonShh, do.label = T,pt.size = 0.2,group.by="res.1.2")

```


```{r,fig.height=15,fig.width=40}

DoHeatmap(object = E15E16P1P4_wt_nonShh, genes.use = c("Ano1","Cftr","Mki67","Top2a","Sox9","Col11a1","Col11a2","Acan","Col2a1","Mia","Wif1","Wnt2","Cd34","Tek","Smoc2","Pi16","Ly6a","Thy1","Lum","Dcn","Ppp1r14a","Ednrb","Cdh4","Acta2","Tagln","Myh11","Rgs5","Notch3","C1qa","Fcer1g","Cd3g","Pecam1","Lyve1","Ascl1","Chga","Tubb3","Snap25","Plp1","Mpz","Dbi","Adipoq","Fabp4","Alas2"), 
    slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.by="res.1.2",group.cex = 25,cex.row=25
  )
```

```{r}
res.used <- 1.6

E15E16P1P4_wt_nonShh <- FindClusters(object = E15E16P1P4_wt_nonShh, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE)
```
```{r}
E15E16P1P4_wt_nonShh <- RunTSNE(object = E15E16P1P4_wt_nonShh, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2,k.param=10)

```

```{r}
TSNEPlot(object = E15E16P1P4_wt_nonShh, do.label = T,pt.size = 0.2,group.by="res.1.6")

```
```{r,fig.height=18,fig.width=60}

DoHeatmap(object = E15E16P1P4_wt_nonShh, genes.use = c("Wnt2","Nrp1","Tek","Ly6a","Thy1","Sox9","Acan","Col2a1","Acta2","Tagln","Myh11","Ppp1r14a","Rgs5","Notch3","Pecam1","Lyve1","Fcer1g","C1qa","Cd3g","Adipoq","Fabp4","Plp1","Mpz","Ascl1","Chga","Snap25","Mki67"), 
    slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.by="res.1.6",group.cex = 30,cex.row=35
  )
```

##### We will use resolution=1.6 for the rest of the analysis.

```{r}
E15E16P1P4_wt_nonShh <- SetAllIdent(object = E15E16P1P4_wt_nonShh, id = "res.1.6")

E15E16P1P4_wt_nonShh_res16_c31<-FindMarkers(E15E16P1P4_wt_nonShh,ident.1=c(31),only.pos = TRUE)
E15E16P1P4_wt_nonShh_res16_c31
```
##### So cluster 31 is likely to be RBC.

```{r}
library(plyr)
E15E16P1P4_wt_nonShh@meta.data$cell_type_1.6<-mapvalues(E15E16P1P4_wt_nonShh@meta.data$res.1.6,from=c("0","1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21","22","23","24","25","26","27","28","29","30","31","32"),to=c("Fibroblast","CyclingFibroblast","Chondrocyte","Fibroblast","Fibroblast","Fibroblast","Fibroblast","Fibroblast","Fibroblast","Fibroblast","CyclingFibroblast","Immune_1","CyclingFibroblast","Muscle","Fibroblast","VascularEndothelial","Muscle","MesenchymalProgenitor","Fibroblast","Chondrocyte","Immune_2","Chondrocyte","CyclingFibroblast","LymphaticEndothelial","Muscle","MesenchymalProgenitor","MesenchymalProgenitor","SchwannCell","MesenchymalProgenitor","VSMC/pericyte","Muscle","RBC","Neuron/NEC"))
```
```{r}
wt_nonShh_cellType1.6<-E15E16P1P4_wt_nonShh@meta.data$cell_type_1.6
names(wt_nonShh_cellType1.6)<-E15E16P1P4_wt_nonShh@cell.names
```

```{r}
save(E15E16P1P4_wt_nonShh,file="seuratE15E16P1P4_wt_nonShh.RData")
```

```{r}
#load("seuratE15E16P1P4_wt_nonShh.RData")
```

```{r,fig.height=18,fig.width=60}

DoHeatmap(object = E15E16P1P4_wt_nonShh, genes.use = c("Sox9","Acan","Col2a1","Acta2","Tagln","Myh11","Ppp1r14a","Rgs5","Notch3","Wnt2","Tek","Tek","Ly6a","Thy1","Pecam1","Lyve1","Fcer1g","C1qa","Cd3g","Plp1","Mpz","Ascl1","Chga","Snap25","Mki67"), 
    slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.by="cell_type_1.6",group.cex = 40,cex.row=35,group.order = c("Chondrocyte","Fibroblast","CyclingFibroblast","Muscle","VSMC/pericyte","MesenchymalProgenitor","LymphaticEndothelial","VascularEndothelial","Immune_1","Immune_2","SchwannCell","Neuron/NEC"),cells.use=E15E16P1P4_wt_nonShh@cell.names[!(E15E16P1P4_wt_nonShh@meta.data$res.1.6 %in% c(31))])
```
##### For the purpose of visualization, we average within each cell type:

```{r}
E15E16P1P4_wt_nonShh<-SetAllIdent(object = E15E16P1P4_wt_nonShh, id = "cell_type_1.6")
average_wt_nonShh_res1.6_Annotation<-AverageExpression(object = E15E16P1P4_wt_nonShh,return.seurat = T)
```
```{r,fig.height=8,fig.width=7}

DoHeatmap(object = average_wt_nonShh_res1.6_Annotation, genes.use = c("Sox9","Acan","Col2a1","Twist2","Acta2","Tagln","Myh11","Ppp1r14a","Rgs5","Notch3","Wnt2","Tek","Tek","Ly6a","Thy1","Pecam1","Lyve1","Fcer1g","C1qa","Cd3g","Plp1","Mpz","Ascl1","Chga","Snap25","Mki67"), 
    slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.cex = 25,cex.row=12,group.order = c("Chondrocyte","Fibroblast","CyclingFibroblast","Muscle","VSMC/pericyte","MesenchymalProgenitor","LymphaticEndothelial","VascularEndothelial","Immune_1","Immune_2","SchwannCell","Neuron/NEC"),cells.use=average_wt_nonShh_res1.6_Annotation@cell.names[!(average_wt_nonShh_res1.6_Annotation@cell.names %in% c("RBC"))])
```
```{r,fig.width=5,fig.height=5}
ggplot(data=E15E16P1P4_wt_nonShh@meta.data[!(E15E16P1P4_wt_nonShh@meta.data$res.1.6 %in% c(31)),],aes(age,fill=cell_type_1.6))+ 
    geom_bar(position="fill")+ theme(axis.text.x = element_text(angle = 45, hjust = 1))
```
```{r}
table(E15E16P1P4_wt_nonShh@meta.data$age[!(E15E16P1P4_wt_nonShh@meta.data$res.1.6 %in% c(31))],E15E16P1P4_wt_nonShh@meta.data$cell_type_1.6[!(E15E16P1P4_wt_nonShh@meta.data$res.1.6 %in% c(31))])
```

##### neuron/NEC, and if they are from E15 or P4, are they from Shh lineage (if from Shh lineage, they should be captured in the "green" gate):
##### scaled data:
```{r,fig.height=3,fig.width=10}

DoHeatmap(object = E15E16P1P4_wt_nonShh, genes.use = c("Epcam","Snap25","Tubb3","Chga","Ascl1","Ret","Insm1","Phox2a","Phox2b","Mpz","Sox10","Shh"), 
    slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.by="gate",group.cex = 30,cex.row=16,cells.use=E15E16P1P4_wt_nonShh@cell.names[E15E16P1P4_wt_nonShh@meta.data$cell_type_1.6 %in% c("Neuron/NEC")])
```
##### unscaled data:
```{r,fig.height=3,fig.width=10}

DoHeatmap(object = E15E16P1P4_wt_nonShh, genes.use = c("Epcam","Snap25","Tubb3","Chga","Ascl1","Ret","Insm1","Phox2a","Phox2b","Mpz","Sox10","Shh"), 
    slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = F,group.by="gate",group.cex = 30,cex.row=12,cells.use=E15E16P1P4_wt_nonShh@cell.names[E15E16P1P4_wt_nonShh@meta.data$cell_type_1.6 %in% c("Neuron/NEC")])
```
##### Schwann cell:
```{r,fig.height=3,fig.width=10}

DoHeatmap(object = E15E16P1P4_wt_nonShh, genes.use = c("Sox10","Gap43","Blbp","Mpz","Dhh","P75ntr","S100","Egr2","Mbp","Ncam1"), 
    slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.by="age",group.cex = 30,cex.row=16,cells.use=E15E16P1P4_wt_nonShh@cell.names[E15E16P1P4_wt_nonShh@meta.data$cell_type_1.6 %in% c("SchwannCell")])
```

##### some lineage markers:
```{r,fig.height=20,fig.width=20}
VlnPlot(object = E15E16P1P4_wt_nonShh, features.plot = c("Sox10","Mpz","Shh","Phox2a","Phox2b"), nCol = 1,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="cell_type_1.6")

```
##### Chondrocytes across time:
```{r,fig.height=12,fig.width=50}

DoHeatmap(object = E15E16P1P4_wt_nonShh, genes.use = c("Col11a1","Mia","Sox9","Sox5","Sox6","Acan","Col2a1","Msx2","Lect1","Hoxa5","Top2a","Mki67","Runx1","Runx2","Col8a1","Col8a2"), 
    slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.by="res.1.6",group.cex = 45,cex.row=40,cells.use=E15E16P1P4_wt_nonShh@cell.names[E15E16P1P4_wt_nonShh@meta.data$cell_type_1.6 %in% c("Chondrocyte")],group.order = c(19,21,2))
```


```{r}
library(ggalluvial)
```

```{r, fig.height=3, fig.width=4}
ggplot(data=E15E16P1P4_wt_nonShh@meta.data[E15E16P1P4_wt_nonShh@meta.data$res.1.6 %in% c("19","21","2"),],aes(axis1=res.1.6,axis2=age))+geom_alluvium(aes(fill=age))+geom_stratum(width = 1/12, fill = "black", color = "grey") +geom_label(stat = "stratum", label.strata = TRUE)+scale_x_discrete(limits = c("res.1.6", "age"), expand = c(.05, .05))
```
##### Don't expect a lot of Col10a1 or Mmp13:
```{r,fig.height=6,fig.width=12}
VlnPlot(object = E15E16P1P4_wt_nonShh, features.plot = c("Col10a1","Mmp13"), nCol = 2,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="cell_type_1.6")

```
```{r,fig.height=4,fig.width=5}
VlnPlot(object = E15E16P1P4_wt_nonShh, features.plot = c("orig.CellCycle_score"), nCol = 3,x.lab.rot = T,point.size.use = 0.3,use.raw=F,group.by="age",ident.include = c(19,2,21))

```
```{r, fig.height=20, fig.width=16}
ggplot(data=E15E16P1P4_wt_nonShh@meta.data,aes(axis1=orig.1.4,axis2=res.1.6,axis3=cell_type_1.6))+geom_alluvium(aes(fill=res.1.6))+geom_stratum(width = 1/12, fill = "black", color = "grey") +geom_label(stat = "stratum", label.strata = TRUE)+scale_x_discrete(limits = c("orig.1.4", "res.1.6","cell_type_1.6"), expand = c(.05, .05))
```
```{r, fig.height=15, fig.width=10}
ggplot(data=E15E16P1P4_wt_nonShh@meta.data,aes(axis1=orig.1.4,axis2=cell_type_1.6))+geom_alluvium(aes(fill=cell_type_1.6))+geom_stratum(width = 1/12, fill = "black", color = "grey") +geom_label(stat = "stratum", label.strata = TRUE)+scale_x_discrete(limits = c("orig.1.4", "res.1.6"), expand = c(.05, .05))
```
























